C. elegans mutant identification with a one-step whole-genome-sequencing and SNP mapping strategy.

Whole-genome sequencing (WGS) is becoming a fast and cost-effective method to pinpoint molecular lesions in mutagenized genetic model systems, such as Caenorhabditis elegans. As mutagenized strains contain a significant mutational load, it is often still necessary to map mutations to a chromosomal interval to elucidate which of the WGS-identified sequence variants is the phenotype-causing one. We describe here our experience in setting up and testing a simple strategy that incorporates a rapid SNP-based mapping step into the WGS procedure.

Analysis of multiple ethyl methanesulfonate-mutagenized Caenorhabditis elegans strains by whole-genome sequencing.

Whole-genome sequencing (WGS) of organisms displaying a specific mutant phenotype is a powerful approach to identify the genetic determinants of a plethora of biological processes. We have previously validated the feasibility of this approach by identifying a point-mutated locus responsible for a specific phenotype, observed in an ethyl methanesulfonate (EMS)-mutagenized Caenorhabditis elegans strain. Here we describe the genome-wide mutational profile of 17 EMS-mutagenized genomes as assessed with a bioinformatic pipeline, called MAQGene.

Cis-regulatory mutations in the Caenorhabditis elegans homeobox gene locus cog-1 affect neuronal development.

We apply here comparative genome hybridization as a novel tool to identify the molecular lesion in two Caenorhabditis elegans mutant strains that affect a neuronal cell fate decision. The phenotype of the mutant strains resembles those of the loss-of-function alleles of the cog-1 homeobox gene, an inducer of the fate of the gustatory neuron ASER. We find that both lesions map to the cis-regulatory control region of cog-1 and affect a phylogenetically conserved binding site for the C2H2 zinc-finger transcription factor CHE-1, a previously known regulator of cog-1 expression in ASER.

Online tools for predicting integral membrane proteins.

We identify and describe a set of tools readily available for integral membrane protein prediction. These tools address two problems: finding potential transmembrane proteins in a pool of new sequences, and identifying their transmembrane regions. All methods involve comparing the query protein against one or more target models.

Membrane protein prediction methods.

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling.

PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins.

PROFtmb predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria. For each query protein, PROFtmb provides both a Z-value indicating that the protein actually contains a membrane barrel, and a four-state per-residue labeling of upward- and downward-facing strands, periplasmic hairpins and extracellular loops. While most users submit individual proteins known to contain TMBs, some groups submit entire proteomes to screen for potential TMBs.

Predicting transmembrane beta-barrels in proteomes.

Very few methods address the problem of predicting beta-barrel membrane proteins directly from sequence. One reason is that only very few high-resolution structures for transmembrane beta-barrel (TMB) proteins have been determined thus far. Here we introduced the design, statistics and results of a novel profile-based hidden Markov model for the prediction and discrimination of TMBs.

CisOrtho: a program pipeline for genome-wide identification of transcription factor target genes using phylogenetic footprinting.

Background: All known genomes code for a large number of transcription factors. It is important to develop methods that will reveal how these transcription factors act on a genome wide level, that is, through what target genes they exert their function.

Results: We describe here a program pipeline aimed at identifying transcription factor target genes in whole genomes.